Biotechnological and Chemical Sciences in Diagnostics

In vitro diagnostics and data analysis

In vitro diagnostics and data analysis

Academic year 2024/2025

Sommario del corso

• Course objectives
• Results of learning outcomes
• Program
• Course delivery
• Learning assessment methods
• Suggested readings and bibliography
• Teaching tools
• Course card

Course objectives

Data analysis (Vincenti)

Learning of the most common statistical tools to conduct multivariate chemometric analyses of large datasets. Ability to develop an original chemometric strategy to tackle complex problems of experimental design, classification, and regression (including calibration), by means of the correct choice of statistical approaches. Ability to use a suitable statistical strategy to obtain grounded decision making policies, based on sound inferential procedures. Ability to apply the acquired theoretical concepts to real cases of diagnostic analysis.

In vitro diagnostics (Anfossi, Di Nardo)

Knowledge of the principles of the analytical methods underlying the common diagnostic devices. Knowledge of the principal detection strategies adopted for the development of in vitro diagnostics. Ability to recognize limits and potential of the different methodological approaches, also as a function of the analytical requirements. Ability to apply the theoretical concepts to examples of applications of in vitro diagnostics.

Results of learning outcomes

Data analysis (Vincenti)

The module recalls fundamental concepts of linear algebra and express the new statistical content with substantial in-depth mathematical abstraction. Therefore, the program explained in classroom should necessarily be integrated by personal and collective thinking over during homework. At the end of this process, the students should exhibit adequate control of the subject and be able to describe the different topics with appropriate terms, following orderly, rational, and consequent sequences of cause and effect. They should also be able to apply the learned concepts to contexts of practical application in diagnostics.

In vitro diagnostics (Anfossi, Di Nardo)

1. Knowledge and understanding: the students should exhibit adequate knowledge of the principle of the analytical methods, of detection strategies and of the principal platforms of vitro diagnostics
2. Ability to apply knowledge and understanding: The students should be able to apply the learned concepts to contexts of practical application.
3. Autonomy of judgment: The students should be able to critically discuss limitations and fields of application of the different analytical approaches and platforms commonly adopted for in vitro diagnostics development
4. Communication skills: The students should be able to describe the different topics with appropriate terms, following orderly, rational, and consequent sequences of cause and effect
5. Learning abilities: The students should be able to connect arguments and to propose solutions to simple problems of the application of diagnostics to clinically relevant biomarkers

Program

Data analysis (Vincenti)

The module introduces the most effective statistical and chemometric techniques for multivariate analysis of experimental data and decision making.

Definition and methods to approach complex systems and their multivariate structure. Role of chemometrics in the recognition of holistic effects and macro-properties of complex systems. Data organization in matrices. Mathematical transformation and scaling of data. Concepts of distance between objects, similarity, correlation, and covariance. Analysis of variance. Principal components analysis and its graphical representations. Methods of cluster analysis. Experimental design. Objective function and its modelling as a function of the experimental parameters. Regression techniques and model testing. Analytical methods' validation: calibration, limits-of-detection, accuracy, matrix effect and recovery. Classification analysis and class-modelling. Confusion and loss matrices. Weighted Gini impurity. Receiver operating characteristic curves. Prior odds in Bayesian statistics. Classification methods: SIMCA, K-NN, discriminant analysis, naïve Bayes, decision trees, random forest, logistic regression. Single and multiple ordinary least squares regression.  Examples of real diagnostic applications.

In vitro diagnostics (Anfossi, Di Nardo)

Principle of analytical methods based on molecular recognition properties: immunological, molecular, and enzymatic methods. Formats and methodological approaches: direct and indirect, homogeneous and heterogeneous.

Principle of electrophoresis on solid support and in capillary.

Detection strategies: scattering (turbidimetry and nephelometry), reflectance photometry, luminescence phenomena (chemiluminescence and bioluminescence), electrochemical measurements. Nature and properties of the main probes (enzymes, fluorophores, chemiluminescent molecules, beads, nanoparticles).

Point of care tests: principles, set-up, and operation.

Qualitative and quantitative testing.

Validation of in vitro-diagnostics

Examples of methods used for the measurements of clinically relevant biomarkers (tumor markers, infectious diseases diagnosis, hormones, biomarkers of chronic pathologies, etc).

Course delivery

Data analysis (Vincenti)

The module includes 24 hours of classroom teaching (3 CFU). Recap activities in groups of 3 students are promoted. The teaching material plus supporting materials are made available on Moodle.

In vitro diagnostics (Anfossi, Di Nardo)

The module includes 32 hours of classroom teaching (4 CFU). The teaching material (slides), supporting materials and sample exam questions are made available on Moodle.

Learning assessment methods

Data analysis (Vincenti)

An oral examination of the candidate is conducted along 45-60 minutes, and represents a significant stage of individualized teaching. The student is asked to illustrate theroretical concepts in a logical sequence, to express definitions using appropriate nomenclature, to answer to specific questions, to logically deduce consequences and/or practical operating conditions from the learned theoretical concepts. The aspects of narrative coherence, phenomenology understanding, and deducing proficiency are considered at the highest grade. In order to develop this expertise, it is strongly recommended to study in groups (not individually), so as to allow wide discussion of concepts, comparison of interpretations, and reciprocal explanation of content and ideas. Ultimate objective of the assessment is to make evident to the student how deep is its theoretical knowledge, how this knowledge has been translated into real expertise, and how much he/she is capable of transferring this competence to somebody else.

In vitro diagnostics (Anfossi, Di Nardo)

An oral examination of the candidate is conducted along 30-40 minutes. The examination takes place in the same session as the learning assessment of the "Chemometrics".

The student is asked to illustrate concepts by using appropriate language, to answer to specific questions, and to logically connect concepts.

The final grade will be assessed based on the weighted average of the grades of the tests.

The presentations slides together with depth study papers are available in the e-learnig UniTO moodle website

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